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Advancing DIA-Based Limited Proteolysis Workflows: Introducing DIA-LiPA.

Chloé Van Leene1,2, Emin Araftpoor1,2, An Staes1,2,3

  • 1VIB UGent Center for Medical Biotechnology, B9052 Ghent, Belgium.

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|February 14, 2026
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Summary
This summary is machine-generated.

We developed DIA-LiPA, a new data analysis pipeline for limited proteolysis coupled to mass spectrometry (LiP-MS). This method enhances the study of protein dynamics by improving data interpretation and uncovering regulatory patterns.

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Area of Science:

  • Proteomics
  • Structural Biology
  • Biochemistry

Background:

  • Limited proteolysis coupled to mass spectrometry (LiP-MS) is valuable for probing protein conformational dynamics.
  • Interpreting LiP-MS data is challenging due to heterogeneous cleavage and missing data.
  • Advances in data-independent acquisition (DIA) and machine learning offer potential improvements but require evaluation in LiP-MS.

Purpose of the Study:

  • To systematically evaluate library-free DIA workflows for LiP-MS.
  • To introduce a novel DIA-based Limited Proteolysis data Analysis pipeline (DIA-LiPA).
  • To provide a robust framework for mechanistic insights into protein dynamics.

Main Methods:

  • Systematic evaluation of library-free DIA workflows (DIA-NN, Spectronaut) using human and yeast cell lysates.
  • Benchmarking identification depth, reproducibility, and false discovery rate.
  • Development and validation of the DIA-LiPA workflow integrating semitryptic/tryptic data and accounting for missingness.

Main Results:

  • Library-free DIA approaches demonstrate high sensitivity and reproducibility for LiP-MS.
  • DIA-LiPA successfully reproduces known structural signatures across multiple datasets.
  • The pipeline uncovers additional regulatory patterns, enhancing structural interpretation.

Conclusions:

  • Library-free DIA workflows are effective for LiP-MS, reducing experimental overhead.
  • DIA-LiPA offers a robust and sensitive framework for analyzing LiP-MS data.
  • This approach facilitates deeper mechanistic insights into protein conformational dynamics.